Non parametric

For comparing median

set.seed(12345)
ggbetweenstats(
  data = exam,
  x = GENDER,
  y = MATHS,
  type = 'np',
  title = 'Comparison of Maths Scores by gender'
)

usually only want to show the significant ones.

p value smaller than 0.05

ggbetweenstats(
  data = exam,
  x = RACE,
  y = MATHS,
  type = 'np',
  mean.ci = TRUE,
  pairwise.comparisons = TRUE,
  pairwise.display = 's',
  p.adjust.method = 'fdr',
)

## Scatterplot

ggscatterstats(
  data = exam,
  x = ENGLISH,
  y = MATHS,
  label.var = ID,
  label.expression = ENGLISH <30 & MATHS >75,
  title = 'Relationship between English and Maths'
)

ggcoefstats

summarise and present regression parameter estimates - usually in a table

first run lm model using lm function

stats shown = parameter , t-stats , pvalue

load wine dataset

wine <- read_csv('data/wine_quality.csv')

fa <- ggplot(data = wine, aes(x = `fixed acidity`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
va <- ggplot(data = wine, aes(x = `volatile acidity`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
ca <- ggplot(data = wine, aes(x = `citric acid`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
rs <- ggplot(data = wine, aes(x = `residual sugar`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
ch <- ggplot(data = wine, aes(x = `chlorides`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
fsd <- ggplot(data = wine, aes(x = `free sulfur dioxide`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
tsd <- ggplot(data = wine, aes(x = `total sulfur dioxide`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
density <- ggplot(data = wine, aes(x = `density`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
ph  <- ggplot(data = wine, aes(x = `pH`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
sul  <- ggplot(data = wine, aes(x = `sulphates`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')
al  <- ggplot(data = wine, aes(x = `alcohol`)) + geom_histogram(bins = 20, colour = 'black', fill = 'light blue')

ggarrange(fa,va,ca,rs,ch,fsd,tsd,density, ph, sul,al, ncol = 4, nrow = 3)

correlation matrix

several packages available

ggcorrmat from ggstatsplot - 3 things to watch for

colour - divergent colour
cross - indicates that the variables are not statistically significant

benefits - provide statistical test - if you’re interested in finding out how significant etc.

ggcorrmat(
  data = wine,
  cor.vars= c(1:11)
)

correlogram

from the corrplot package

benefits : able to reorder correlation matrix - hclust, alphabetical, PCA, etc. https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html

BUT corrplot cannot understand dataframe - need to use cor function to output correlation matrix first

benefits - more useful if you want to do variable selection as compared to the ggstatsplot package as corrplot enables you to arrange the variables

wine.cor = cor(wine[,1:11]) # need to create correlation matrix first
corrplot(wine.cor,
         method ='ellipse',
         type = 'lower',
         diag = FALSE, #words are not diag
         tl.col = 'black' #text label colour
         )

change method and order

wine.cor = cor(wine[,1:11]) # need to create correlation matrix first
corrplot(wine.cor,
         method ='color',
         type = 'lower',
         diag = FALSE, #words are not diag
         tl.col = 'black', #text label colour
         order = 'AOE'
         )

GGally package

ggpairs(data = wine,
        columns = c(1:11),
        size = 0.1)

## Warning in warn_if_args_exist(list(...)): Extra arguments: "size" are being
## ignored. If these are meant to be aesthetics, submit them using the 'mapping'
## variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

corrgram(wine.cor,
         order = TRUE,
         upper.panel = panel.cor,
         main = 'Correlation')

heatmap

if you dont have alot of variables

consider if you need to scale it or standarise.

wh <- read_csv('data/WHData-2018.csv')

ideally want to have interactive heatmap as you usually have alot of variables

load plotly heatmap

packages = c('heatmaply', 'readr', 'seriation', 'dendextend','parallelPlot')
for (p in packages){
  if(!require(p,character.only = T)){
    install.packages(p)
  }
  library(p, character.only = T)
}

data preparation

row.names(wh) <- wh$Country # country becomes row name

## Warning: Setting row names on a tibble is deprecated.

wh1 <- select(wh, c(3,7:12))
wh_matrix <- data.matrix(wh1)

Heatmaply

avoid using red and green

avoid using diverging colours

heatmaply(normalize(wh_matrix),
          Colv=NA,
          seriate = "none", 
          colors = Blues,
          k_row = 5, # 5 clusters
          margins = c(NA, 200, 60, NA),
          fontsize_row = 4,
          fontsize_col = 5,
          main = 'World Happiness Score and Variables by Country, 2018',
          xlab = 'World Happiness Indicators',
          ylab = 'Countries'
          )

parallel coordinates

kmeans clustering cannot be aggregated upwards or downwards - need to use parallel coordinates

parallelPlot(wh)

histo <- rep(TRUE, ncol(wh)) #create histogram - and plot his over the plot, true refer to all the variables selected - actual function is rep.
parallelPlot(wh,
              histoVisibility = histo)

in-class_ex08

Su Yiin Ang

3/8/2021

In-class_ex08

Load necessary packages

read csv file

Comparison between groups - compare the two means

this package doesn’t filter NA - need to filter out NA first